期刊
FRONTIERS IN MOLECULAR BIOSCIENCES
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2021.634836
关键词
deep learning; prioritization; peptide vaccine design; convolutional neural networks; attention
资金
- National Science Foundation [IIS-1855501, IIS-1827472]
- National Institute of General Medical Sciences [2R01GM118470-05]
- National Library of Medicine [1R01LM012605-01A1, 1R21LM013678-01]
- AWS Machine Learning Research Award
In this study, two allele-specific Convolutional Neural Network-based methods were developed to predict peptide-MHC binding, achieving more accurate binding peptide prioritization and important amino acid identification. Experimental results demonstrated significant improvement over state-of-the-art methods, with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.
T-cell receptors can recognize foreign peptides bound to major histocompatibility complex (MHC) class-I proteins, and thus trigger the adaptive immune response. Therefore, identifying peptides that can bind to MHC class-I molecules plays a vital role in the design of peptide vaccines. Many computational methods, for example, the state-of-the-art allele-specific method MHCflurry, have been developed to predict the binding affinities between peptides and MHC molecules. In this manuscript, we develop two allele-specific Convolutional Neural Network-based methods named ConvM and SpConvM to tackle the binding prediction problem. Specifically, we formulate the problem as to optimize the rankings of peptide-MHC bindings via ranking-based learning objectives. Such optimization is more robust and tolerant to the measurement inaccuracy of binding affinities, and therefore enables more accurate prioritization of binding peptides. In addition, we develop a new position encoding method in ConvM and SpConvM to better identify the most important amino acids for the binding events. We conduct a comprehensive set of experiments using the latest Immune Epitope Database (IEDB) datasets. Our experimental results demonstrate that our models significantly outperform the state-of-the-art methods including MHCflurry with an average percentage improvement of 6.70% on AUC and 17.10% on ROC5 across 128 alleles.
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